| ISBN13: | 9781032206448 |
| ISBN10: | 1032206446 |
| Binding: | Hardback |
| No. of pages: | 232 pages |
| Size: | 234x156 mm |
| Weight: | 590 g |
| Language: | English |
| Illustrations: | 44 Illustrations, black & white; 4 Halftones, black & white; 40 Line drawings, black & white; 45 Tables, black & white |
| 597 |
Electrical engineering and telecommunications, precision engineering
Energy industry
Mathematical Theory of computing
Data management in computer systems
Computer architecture, logic design
Computer networks in general
Database management softwares
Privacy, data security
Safety and health aspects of computing
Environmental sciences
Electrical engineering and telecommunications, precision engineering (charity campaign)
Energy industry (charity campaign)
Mathematical Theory of computing (charity campaign)
Data management in computer systems (charity campaign)
Computer architecture, logic design (charity campaign)
Computer networks in general (charity campaign)
Database management softwares (charity campaign)
Privacy, data security (charity campaign)
Safety and health aspects of computing (charity campaign)
Environmental sciences (charity campaign)
Big Data Analytics in Fog-Enabled IoT Networks
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This book emphasizes and facilitate a greater understanding of various security and privacy approaches using the advance AI and Big data technologies like machine/deep learning, federated learning, blockchain, edge computing and the countermeasures to overcome the vulnerabilities of the Fog-enabled IoT system.
The integration of fog computing with the resource-limited Internet of Things (IoT) network formulates the concept of the fog-enabled IoT system. Due to a large number of IoT devices, the IoT is a main source of Big Data. A large volume of sensing data is generated by IoT systems such as smart cities and smart-grid applications. A fundamental research issue is how to provide a fast and efficient data analytics solution for fog-enabled IoT systems. Big Data Analytics in Fog-Enabled IoT Networks: Towards a Privacy and Security Perspective focuses on Big Data analytics in a fog-enabled-IoT system and provides a comprehensive collection of chapters that touch on different issues related to healthcare systems, cyber-threat detection, malware detection, and the security and privacy of IoT Big Data and IoT networks.
This book also emphasizes and facilitates a greater understanding of various security and privacy approaches using advanced artificial intelligence and Big Data technologies such as machine and deep learning, federated learning, blockchain, and edge computing, as well as the countermeasures to overcome the vulnerabilities of the fog-enabled IoT system.
1. Deep Learning Techniques in Big Data-Enabled Internet-of-Things Devices. 2. IoMT based Smart Health Monitoring: The Future of HealthCare. 3. A Review on Intrusion Detection Systems and Cyber Threat Intelligence for Secure IoT-Enabled Network: Challenges and Directions. 4. Self-Adaptive Application Monitoring for Decentralized Edge Frameworks. 5. Federated Learning and Its Application in Malware Detection. 6. An Ensemble XGBoost Approach for the Detection of Cyber-Attacks in the Industrial IOT Domain. 7. A Review on IoT for the Application of Energy, Environment, and Waste Management: System Architecture and Future Directions. 8. Analysis of Feature Selection Methods for Android Malware Detection Using Machine Learning Techniques. 9. An Efficient Optimizing Energy Consumption Using Modified Bee Colony Optimization in Fog and IoT Networks.
